The present invention is in the field of finance, economics, forecasting, math, general analytics and business statistics, and relates to modeling of business and financial variables. Traditionally, analysts have used methods such as multiple regression analysis to model the behavior of certain variables. For example, how much sales revenues will be generated by a firm if they spend a certain amount in marketing, hire these many people, if the economy is on a downturn, and if general prices are at a certain level (e.g., a best-fitting regression equation can be generated like Sales=$10 Million+2.1×Marketing Expenses in $Millions, which means that for every additional million dollars spent on marketing expenses, the company will generate an additional $2.1 million, and so forth). When historical data exists, the analyst can use regression analysis to model and determine the best-fitting equation to help forecast, predict, and model the behavior of these variables. Regression analysis can be used in any application, from business and economics to finance and engineering, as well as the social and physical sciences. The idea behind regression analysis is to determine the equation that is the best-fitting model given a set of data. The problem is that regression analysis can be a very difficult field to master. In fact, the depth of regression analysis can be fairly intimidating and can reach the heights of an advanced doctoral degree. This detailed and advanced study of regression analysis is termed econometrics.
The basic regression analysis methods has been applied in many industries and settings, and are widely taught at universities. Econometrics, in contrast, is an area of study that is very specialized. To illustrate, the most basic tests in econometrics bear intimidating names (and equally intimidating and intractable mathematical expressions) such as multicollinearity, micronumerosity, impulse response function, autoregressive integrated moving average, asymmetrical generalized autoregressive conditional heteroskedasticity, and many others. To the regular analyst, such methods would be intractable and often times unused, albeit their power and value of these models provide will far surpass anything that can be done manually. The present invention is named Autoeconometrics, which is the business process method used to test thousands and millions of model combinations and permutations to find the best-fitting equation. As the field of econometrics is wide-ranging, there are certain other methods with similar sounding names as Autoeconometrics but are completely different methods and approaches. Terms like autocorrelation and autoregression are very different. Autocorrelation refers to a set of data that is correlated to itself (e.g., sales in January are related to sales in December, which are related to the sales the previous month) and autoregressive models are used to test and model data that exhibit autocorrelation. Autocorrelation is an observation that data is correlated to itself in the past, autoregression (a.k.a. autoregressive) is the model used to model autocorrelation. Clearly this is very different from the Autoeconometrics described in this document, which is a business process method to find the best-fitting model, and one of these variables might be an autoregressive model.
This document details the invention of a business process technique called Autoeconometrics whereby thousands and even millions of different model combinations are automatically tested in a specialized algorithm with its preferred embodied as a software application module. This process and method automatically runs through many econometric variations using a sophisticated set of computer algorithms and business processes to determine the best-fitting equation or model that best explains the data under analysis.